Luis Villarrubia and Alex Acero
In this paper we describe a technique for non-keyword
rejection and we will evaluate in the context of an
audiotex service using the ten Spanish digits. The
baseline keyword recognition system is a
speaker-independent continuous density Hidden Markov
Model recognizer. We propose the use of an affine
transformation to the log-probability of the garbage
model, an HMM model trained to account for both nonkeyword
speech and non-stationary telephone noises. The
parameters of the transformation for the case of isolated
keywords are chosen to minimize a cost function that
weighs the keyword error rate, keyword rejection rate
and false acceptance rate according to the a priori
probabilities of keywordhon-keyword and the
requirements of the specific application. This technique
was also extended to embedded keywords (word-spotting).
Use of this rejection technique on the audiotex
application reduced the total cost function up to 20% for
isolated-word case and 12% for the word-spotting case.
In Proc. of the International Conference on Acoustics, Speech and Signal Processing
Publisher Institute of Electrical and Electronics Engineers, Inc.
© 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.